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          <h1 class="post-title" itemprop="name headline">论文笔记：Graph Convolutional Networks with Argument-Aware Pooling for Event Detection</h1>
        

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<p>Written By Mars at 2019/09/04 18:30   </p>
<p>Reference：<a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16329" target="_blank" rel="noopener">Graph Convolutional Networks with Argument-Aware Pooling for Event<br>  Detection</a></p>
<h1 id="Summary"><a href="#Summary" class="headerlink" title="Summary"></a>Summary</h1><p>本文提出一个基于句法依存树的图卷积神经网络模型用于事件检测，解决了长距离非连续的信息依赖问题。<strong>这个模型最大的创新点是首次在神经网络中引入句法结构特征，在句法依存图上执行卷积操作。</strong>句法依存图是通过句法依存树转换得到的——有向边变为无向边且添加自循环边，然后在句法依存图上建模图卷积网络（GCN）进行触发词的识别和分类。GCN模型大致有两种结构：一是先做图embedding转化成序列格式，然后执行传统的平面卷积操作；二是借鉴卷积核局部加权求和的思想，对图中的任一顶点及其邻域进行加权求和，以达到抽取特征的目的。本文采用的是第二种结构的GCN模型，通过对候选词及其句法邻域词进行卷积操作，建模最直接的信息依赖。<strong>此外，本文还提出了基于实体提及的池化操作（ENTITY），进一步提升了事件检测的效果。</strong></p>
<h1 id="Research-Objective"><a href="#Research-Objective" class="headerlink" title="Research Objective"></a>Research Objective</h1><p>使用GCN来进行事件抽取（触发词的识别和分类）。</p>
<h1 id="Problem-Statement"><a href="#Problem-Statement" class="headerlink" title="Problem Statement"></a>Problem Statement</h1><ol>
<li>长距离非连续的信息依赖问题：如下图所示，有两个一样的触发词”fired”，但是它们的类型不同，第一个”fired”的类型是<strong><em>Attack</em></strong>，第二个”fired”的类型是<strong><em>End-Position</em></strong>。在这个例子中，针对第二个”fired”，不同三元组的选择，例如选择”car was fired”而不是”officer was fired”，则会将”fired”识别为<strong><em>Attack</em></strong>类型，这也是造成NCNN等模型在这种情况下无法预测出正确的事件类型<strong><em>End-Position</em></strong>的原因之一；</li>
<li>实体类别影响事件触发词的类型：在判断”fired”的类型的时，”officer”以及”car”这两个与”fired”相关的实体类别对于判断”fired”的类型有重要作用（VEHICLE：”car”，PERSON：”officer”）。</li>
</ol>
<p><img src="https://res.cloudinary.com/leomars/image/upload/v1567598424/dependent_tree_nlim51.jpg" alt></p>
<h1 id="Method-s"><a href="#Method-s" class="headerlink" title="Method(s)"></a>Method(s)</h1><p>针对以上两个问题，文章分别提出了对应的解决方法：</p>
<ol>
<li>采用了GCN模型来处理长距离非连续的信息依赖问题；</li>
<li>Entity Mention；<ul>
<li>在输入特征中加入实体类型信息（实体类型embedding）；</li>
<li>基于实体提及的池化操作（ENTITY）。</li>
</ul>
</li>
<li>针对实体之间的关系的不同对触发词识别的作用差异提出了边权重。</li>
</ol>
<h1 id="Evaluation"><a href="#Evaluation" class="headerlink" title="Evaluation"></a>Evaluation</h1><p>数据集：ACE2005，TAC KBP 2015</p>
<p>评价指标：P，R，F1</p>
<p>实验分为两个部分：</p>
<ol>
<li>根据输入特征有没有经过BiLSTM和GCN的层数K（即调数）对模型进行了测试；</li>
<li>对本文提出的基于实体提及的池化操作和另外三类常见的池化操作进行了对比；</li>
<li>Baseline对比。</li>
</ol>
<h1 id="Conclusion"><a href="#Conclusion" class="headerlink" title="Conclusion"></a>Conclusion</h1><p>该论文提出一个基于句法依存树的图卷积神经网络模型用于事件检测，解决了长距离非连续的信息依赖问题，该模型在两个常用数据集（ ACE 2005和TAC KBP 2015）上取得了最好的结果。</p>
<h1 id="Notes"><a href="#Notes" class="headerlink" title="Notes"></a>Notes</h1><ol>
<li>本文没有给出模型图，这里开篇给出一个简单的模型图，以助于理解；</li>
<li>论文代码复现：<ul>
<li>word embeddings, position embeddings and entity type embeddings；</li>
<li>获取句法依存图，Dependency parsing。</li>
</ul>
</li>
</ol>

      
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